A Meta-Learning Approach for Combining Multiple Classifiers

Abstract

This paper presents an approach for building a multi-classifier system in a Mean Field Genetic Algorithm (MGA)-based inductive learning environment. Multiple base classifiers are combined to build a multi-classifier system. A base classifier consists of a general classifier and a meta-classifier. The general classifier performs regular classification task. The meta-classifier evaluates classification result of its general classifier and decides whether the base classifier participates into a final decision-making process or not. MGA is a hybrid algorithm of Mean Field Annealing (MFA) and Simulated annealinglike Genetic Algorithm (SGA). It combines benefit of rapid convergence property of MFA and effective genetic operations of SGA.

A Study of Algorithm Selection in Data Mining using Meta-Learning

“…Kim and Hong [15] used the meta-learning approach to create a multi classifier system in order to improve classifier's performance. In order to apply meta-learning, they generated the base classifiers by executing the meanfield genetic algorithm multiple times on the training dataset.…”